Abstract Schizophrenia (SCZ) and major depressive disorder (MDD) are highly heritable, debilitating diseases with lifetime prevalences of ~1% and 15%, respectively. Both disorders carry substantial morbidity and mortality and are associated with severe societal and personal costs. Despite the availability of efficacious treatments for both disorders, ~1/3 of individuals will not achieve symptomatic improvement even after multiple rounds of medication. Identifying individuals at greater risk for such treatment nonresponse, or treatment resistance, could facilitate more targeted interventions for these individuals. A burgeoning literature has identified genomic variation associated with treatment response. In particular, antidepressant response has been suggested to be highly heritable; convergent data from rodent studies likewise suggest that antipsychotic and antidepressant response phenotypes are influenced by genetic variation. However, treatment studies to date have had minimal success in identifying variants associated with psychotropic response, likely as a result of limited sample sizes: prior efforts required sequential treatment trials and prospective assessment to characterize outcomes. Longitudinal electronic health records (EHR) data provide an opportunity to efficiently characterize treatment response in many individuals in real-world settings. Coupled with large and expanding biobanks, these cohorts allow for low- cost, large-scale genomic studies that finally achieve sufficient power to detect realistic effect sizes. The investigators now propose to apply these approaches to the EHRs of two large regional health systems, each linked to a large biobank, to investigate treatment resistance in SCZ and MDD. They will apply canonical indicators of treatment resistance - clozapine treatment for SCZ, and electroconvulsive therapy (ECT) for MDD - to identify coded and uncoded clinical features associated with high probability of treatment resistance in EHR data. These predictors will themselves provide a useful baseline for identifying high risk individuals. Then, they will apply these to study the entire affected population of each biobank, extending existing genomic data with additional genome-wide association, yielding more than 25,000 antidepressant-treated individuals and 2,200 antipsychotic-treated individuals. Rather than simply conducting a case-control study, they will examine treatment resistance as a quantitative trait, applying a method developed by the investigators and shown to substantially increase power for such traits. The project combines expertise in clinical informatics, machine learning, and analysis of large scale genomics, as well as domain-specific expertise in psychiatric treatment resistance. Spanning two distinct health systems, the algorithms and methods developed have maximal portability, facilitating next- step investigations. Successful identification of risk variants will facilitate efforts at clinical risk stratification as well as investigation of the biology underlying treatment resistance.